MapleSim is a multidomain system-level simulation tool that helps designers describe and test their physical systems. Similar to Mechanical Expressions, MapleSim provides a simple GUI to describe a system and can then determine the symbolic equations which describe it. MapleSim then uses these equations to produce highly efficient simulation code – which can be directly simulated or exported to the user’s platform of choice.

Users design the system by dragging and dropping components into a 2D block diagram. When multibody component are used, MapleSim simultaneously translates these block diagrams into a 3D visualization. Users can then manipulate whichever model, 2D or 3D, is more convenient at any given time.

Equation output is seen in the bottom of the screen.

MapleSim works hand in hand with Maplesoft’s Maple mathematical software. Once the model is produced, MapleSim converts the system into a Modelica representation. This information is then sent to the Maple mathematical backbone of the program for further processing.

Maple is famous for its ability to parse, simplify, and optimize mathematical equations. Maple understands how to manipulate and simplify symbolic mathematical equations as symbolic mathematical equations, using heuristics, and algorithms. These features can be used to further analyze the (Modelica) equations coming from the model, or (automatically) generate highly efficient simulation code. They are also a key part of MapleSim’s acausal modelling approach:

“With a causal modelling tool, the user needs to worry about signal inputs and outputs for every component – and changing these afterwards requires restructuring the model. However, with an acausal tool, the user just needs to worry about physical connections,” said Chad Schmitke Director of MapleSim development. “For example, a mechanical flange may be driven by a torque or a motion; this must be explicitly defined in a causal system. In MapleSim you don’t have to worry about causality. You generate the model, define the system variables of interest, and MapleSim’s solver will rearrange the equations based on heuristics to determine causality for you.”

MapleSim 3D Simulation.

“In the FEA world of simulation, you typically test the reaction of individual parts of a system to a set of forces. In a lumped parameter modeling system like MapleSim you generally determine what those forces might be. MapleSim quickly simulates the system and determines how it would operate as a whole. The forces involved can then be tested in the FEA framework. MapleSim answers a different question than FEA; MapleSim determines the reactions a pin might experience in a front-end loader during a given maneuver, while FEA tests how the pin would warp or shear under theses forces” clarified Chad.

This means that MapleSim has the flexibility to be useful to many different people in a product development team. The initial overall design of a system can be modeled in the preliminary stage of development before the finer details are nailed down. MapleSim’s advanced analytics can then simplify the system to assist in later design iterations. These equations will also be useful to those testing the system to verify it will operate as designed.

“Although the analysis capabilities provided by the symbolics is significant, their contributions to the efficiency of the simulation code is also key. Many of our customers rely on this speed to perform HIL testing on systems they previously couldn’t run fast enough. As well, these ultra-fast plant models are being used for model predictive control scenarios, where the controller is actually evaluating an online version of the plant model during operation.” said Chad.

Since the creation of MapleSim five years ago and Maple 25 years ago, Maplesoft has seen some big expansion. Recently, Jim Cooper, Maplesoft CEO, and Michael Pisapia, VP of Europe, opened their new Cambridge office to act as a European HQ. Maplesoft’s software is available in over 100 countries.

Their customer base includes big names like NASA, Canadian Space Agency, Boeing, Bosch, MIT, Stanford, Oxford, and the U.S. Department of Energy.